ArticleFrontiers in neuroinformatics2018
Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment.
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Discrimination of generalized anxiety and major depressive disorders by machine learning of resting-state functional MRI.BMC psychiatry · 2026Article
- Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification.Journal of personalized medicine · 2023Article
- A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders.Frontiers in neuroscience · 2023Article
- Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI.Frontiers in aging neuroscience · 2023Article
- Estimating high-order brain functional networks by correlation-preserving embedding.Medical & biological engineering & computing · 2022Article
- Dysfunction in the Interaction of Information Between and Within the Bilateral Primary Sensory Cortex.Frontiers in aging neuroscience · 2022Article
- Identifying depression disorder using multi-view high-order brain function network derived from electroencephalography signal.Frontiers in computational neuroscience · 2022Article
- Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification.Frontiers in neuroscience · 2022Article
- Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study.Brain sciences · 2021Article
- Brain Function Network: Higher Order vs. More Discrimination.Frontiers in neuroscience · 2021Article
- Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification.Frontiers in neuroscience · 2021Article
- Hierarchical Synchronization Estimation of Low- and High-Order Functional Connectivity Based on Sub-Network Division for the Diagnosis of Autism Spectrum Disorder.Frontiers in neuroscience · 2021Article
- Predicting full-scale and verbal intelligence scores from functional Connectomic data in individuals with autism Spectrum disorder.Brain imaging and behavior · 2020Article
- A toolbox for brain network construction and classification (BrainNetClass).Human brain mapping · 2020Article
- Toward a Better Estimation of Functional Brain Network for Mild Cognitive Impairment Identification: A Transfer Learning View.IEEE journal of biomedical and health informatics · 2020Article
- Group Similarity Constraint Functional Brain Network Estimation for Mild Cognitive Impairment Classification.Frontiers in neuroscience · 2020Article
- Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification.Frontiers in cell and developmental biology · 2020Article
- Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks.Frontiers in neuroscience · 2020Article
- MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI.IEEE access : practical innovations, open solutions · 2020Article
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5 authors.
Funding
Abstract
Functional connectivity (FC) network has been becoming an increasingly useful tool for understanding the cerebral working mechanism and mining sensitive biomarkers for neural/mental disease diagnosis. Currently, Pearson's Correlation (PC) is the simplest and most commonly used scheme in FC estimation. Despite its empirical effectiveness, PC only encodes the low-order (i.e., second-order) statistics by calculating the pairwise correlations between network nodes (brain regions), which fails to capture the high-order information involved in FC (e.g., the correlations among different edges in a network). To address this issue, we propose a novel FC estimation method based on Matrix Variate Normal Distribution (MVND), which can capture both low- and high-order correlations simultaneously with a clear mathematical interpretability. Specifically, we first generate a set of BOLD subseries by the sliding window scheme, and for each subseries we construct a temporal FC network by PC. Then, we employ the constructed FC networks as samples to estimate the final low- and high-order FC networks by maximizing the likelihood of MVND. To illustrate the effectiveness of the proposed method, we conduct experiments to identify subjects with Mild Cognitive Impairment (MCI) from Normal Controls (NCs). Experimental results show that the fusion of low- and high-order FCs can generally help to improve the final classification performance, even though the high-order FC may contain less discriminative information than its low-order counterpart. Importantly, the proposed method for simultaneous estimation of low- and high-order FCs can achieve better classification performance than the two baseline methods, i.e., the original PC method and a recent high-order FC estimation method.
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